1. Technical Field
The present invention relates to radio telecommunications; in particular to, receiving digital data symbols or bits by iterative determination of soft estimates of symbols or bits followed by a hard decision as to what symbol or bit was intended.
2. Description of the Related Art
In an iterative radio receiver which involves mutual information exchange between a detector and a decoder (or two decoders), at each iteration, soft estimates (e.g. log-likelihood ratios, LLR) at the output of the decoder are fed back to the detector (or other decoder) for purposes of interference cancellation. As a result, new and hopefully more reliable soft estimates are made available at the output of the decoder after each iteration process.
However in some cases, the interference cancellation process can lead to poorer soft estimates values for certain bits. This can result in error propagation due to incorrect interference cancellation and therefore lead to unstable bit-error rate performance in the following iterations.
One type of detector or decoder is a multiple-input multiple-output MIMO successive interference cancellation SIC detector; another is a turbo decoder.
In MIMO (multiple-input multiple-output) systems, the reuse of the spreading codes leads to high multiple access interference. Successive interference cancellation (SIC) is a well known technique to reduce the multiple access interference in both detectors for single input-single output (SISO), and multiple-input multiple-output (MIMO) systems such as minimum mean squared error MMSE detectors used in (Bell Laboratories layered space time) BLAST™ type receivers.
Successive interference cancellation techniques of symbol based detection reduce the multiple access interference by cancelling already detected users or data streams from the received signal, see for example Guinand, P. S.; Kerr, R. W.; Moher, M., “Serial interference cancellation for highly correlated users”, Communications, Computers and Signal Processing, 1999IEEE Pacific Rim Conference on, pages: 133–136. This is especially important for MIMO systems, where the spreading code reuse leads to high multiple access interference.
As order metric, the least mean-square error (LMSE) criterion, which finds application in minimum mean squared error MMSE detectors is used, as described in Hassibi, B. “A fast square-root implementation for BLAST”, Signals, Systems and Computers, 2000 Conference Record of the Thirty-Fourth Asilomar Conference, Volume: 2, 2000 pages 1255–1259 vol. 2.
In combination with iterative detection and convolutional decoding the performance of such detectors can be improved to some extent, as described in Li, X.; Huang, H.; Foschini, G. J.; Valenzuela, R. A., “Effects of iterative detection and decoding on the performance of BLAST”, Global Telecommunications Conference, 2000. GLOBECOM '00. IEEE, Vol. 2, 2000, pages: 1061–1066vol. 2.
A known type of MIMO detector is a posteriori probability (APP) detector which generates the log likelihood ratios of the received symbols. It performs an exhaustive search through the transmitted symbol candidates and determines the best vector that matches the received symbols. The APP detector is described in Benedetto, S.; Divsaler, D.; Montorsi, G.; Pollara, F., “A soft-input soft-output APP module for iterative decoding of concatenated codes” IEEE Communications Letters, Volume: 1 Issue: 1, Jan. 1997 Page(s): 22–24.